CompTIA Data+ provides your team members with the confidence to make data analysis come to reality.
As the need for data analytics increases and more roles will be required to provide the right context and improve communication of vital business intelligence. The process of collecting, analyzing the data and reporting it on can help drive your company's priorities and guide in business making
CompTIA data+ is an early-career, data analytics certification that provides you with assurance that you can bring your data analysis into life and take decision-making based on data.
Click here to read more about it:
1) The document provides an introduction to a course on data analytics with Python. It outlines the objectives of the course, which are to conceptually understand data analytics using practical examples rather than just procedures.
2) The course will cover defining data and its importance, different types of data analytics, why analytics is important today, how statistics, analytics and data science are related, and why Python is used. It will also explain the four levels of data measurement.
3) The levels of data from lowest to highest are nominal, ordinal, interval, and ratio. The type of data affects what operations and statistical methods can be meaningfully applied. Descriptive, diagnostic, predictive, and prescriptive analytics will also
Improvement as Data Analyst presents business problems, different problem-solving tools (5 Why, Action Priority Chart, Fishbone, and Flow Mapping), and data analysis process.
The document discusses the analytics life cycle and its key phases. It describes the business understanding phase which ensures projects align with objectives and identifies relevant data. The data understanding phase involves gaining familiarity with available data. Data preparation aims to assess and improve data quality for analysis through tasks like cleaning, integration, transformation and reduction. Modeling techniques are selected and models are generated, built and assessed. The primary goal of data understanding is to gain a comprehensive view of available data to inform subsequent phases.
Practical Data Science the WPC Healthcare Strategy for Delivering Meaningful ...Damian R. Mingle, MBA
Learning to make use of Jupyter to document your Data Science process - real time - and in whatever programming language you want! Using this methodology will allow you to provide insights that help your organization make better decisions to solve their business problems.
The document discusses systems analysis and design. It states that system analysis describes what a system should do to meet user needs, while system design specifies how the system will accomplish this through design activities that produce specifications satisfying requirements developed in analysis. The document then provides details on various aspects of systems analysis, design, feasibility, lifecycles and more.
CRISP-DM: a data science project methodologySergey Shelpuk
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides an overview of the key steps and asks questions to determine readiness to move to the next phase of the project. The overall goal is to successfully apply a standard data science methodology to gain business value from data.
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовGeeksLab Odessa
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides examples of the types of activities and questions that should be addressed to successfully complete that phase of the project.
Add on foundation business analysis certificationADD ON TRAINING
The document provides information about an add-on training program that offers certification in business analysis. The training is accredited by the British Computer Society and covers 14 modules aligned to the BCS standards for Foundation in Business Analysis Certification. The certification helps individuals gain industry recognition as a professional business analyst and validate their skills and knowledge in critical analytical concepts. It benefits both employees, by supporting career development and opportunities, and employers, by helping employees gain practical skills and increasing their value.
1) The document provides an introduction to a course on data analytics with Python. It outlines the objectives of the course, which are to conceptually understand data analytics using practical examples rather than just procedures.
2) The course will cover defining data and its importance, different types of data analytics, why analytics is important today, how statistics, analytics and data science are related, and why Python is used. It will also explain the four levels of data measurement.
3) The levels of data from lowest to highest are nominal, ordinal, interval, and ratio. The type of data affects what operations and statistical methods can be meaningfully applied. Descriptive, diagnostic, predictive, and prescriptive analytics will also
Improvement as Data Analyst presents business problems, different problem-solving tools (5 Why, Action Priority Chart, Fishbone, and Flow Mapping), and data analysis process.
The document discusses the analytics life cycle and its key phases. It describes the business understanding phase which ensures projects align with objectives and identifies relevant data. The data understanding phase involves gaining familiarity with available data. Data preparation aims to assess and improve data quality for analysis through tasks like cleaning, integration, transformation and reduction. Modeling techniques are selected and models are generated, built and assessed. The primary goal of data understanding is to gain a comprehensive view of available data to inform subsequent phases.
Practical Data Science the WPC Healthcare Strategy for Delivering Meaningful ...Damian R. Mingle, MBA
Learning to make use of Jupyter to document your Data Science process - real time - and in whatever programming language you want! Using this methodology will allow you to provide insights that help your organization make better decisions to solve their business problems.
The document discusses systems analysis and design. It states that system analysis describes what a system should do to meet user needs, while system design specifies how the system will accomplish this through design activities that produce specifications satisfying requirements developed in analysis. The document then provides details on various aspects of systems analysis, design, feasibility, lifecycles and more.
CRISP-DM: a data science project methodologySergey Shelpuk
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides an overview of the key steps and asks questions to determine readiness to move to the next phase of the project. The overall goal is to successfully apply a standard data science methodology to gain business value from data.
AI&BigData Lab 2016. Сергей Шельпук: Методология Data Science проектовGeeksLab Odessa
This document outlines the methodology for a data science project using the Cross-Industry Standard Process for Data Mining (CRISP-DM). It describes the 6 phases of the project - business understanding, data understanding, data preparation, modeling, evaluation, and deployment. For each phase, it provides examples of the types of activities and questions that should be addressed to successfully complete that phase of the project.
Add on foundation business analysis certificationADD ON TRAINING
The document provides information about an add-on training program that offers certification in business analysis. The training is accredited by the British Computer Society and covers 14 modules aligned to the BCS standards for Foundation in Business Analysis Certification. The certification helps individuals gain industry recognition as a professional business analyst and validate their skills and knowledge in critical analytical concepts. It benefits both employees, by supporting career development and opportunities, and employers, by helping employees gain practical skills and increasing their value.
Unit 2 Classical Systems Development Methodology.pptxVrundaPatadia
This document discusses classical systems development methodology. It describes the classical systems development life cycle (SDLC) which includes 7 phases: preliminary investigation, determination of system requirements, design of system, development of software, system testing, implementation and evaluation, and system maintenance. It also discusses tools and techniques used in each phase like fact finding techniques, documentation tools, and decision models.
Doing Analytics Right - Designing and Automating AnalyticsTasktop
There is no “one-sized fits all” of development analytics. It is not as simple as “here are the measures you need, go implement them.” The world of software delivery is too complex, and software organizations differ too significantly, to make it that simple. As discussed in the first webinar, the analytics you need depend on your unique business goals and environment.
That said, the design of your analytics solution will still require:
* The dashboards,
* the required data, and
* an appropriate choice of analytical techniques and statistics to apply to the data.
This webinar will describe a straightforward method for finding your analytic solution. In particular, we will explain how to adapt the Goal, Question, Metric (GQM) method to development processes. In addition, we will explain how to avoid “the light is brighter here” analytics anti-pattern: the idea that organizations tend to design metrics programs around the data they can easily get, rather than figuring out how to get the data they really need.
The document provides an overview of changes to the requirements definition process at an internal company. Key points:
- A new, standardized requirements definition process is being implemented across the company to better align requirements with business needs and strategy.
- Training sessions on the new process will be held in July and August, and recorded versions will be made available.
- The process aims to reduce risks and costs through clear documentation and prioritization of requirements.
- Roles and responsibilities in the new process are defined, including for business analysts.
Building a Marketing Data Warehouse from Scratch - SMX Advanced 202Christopher Gutknecht
This deck covers the journey of starting with BigQuery, adding more data sources and building a process around your data warehouse. It covers the three phases greenfield, dashboards and operational analytics and the necessary data components.
The code for uploading your product feed can be found here:
https://gist.github.com/ChrisGutknecht/fde93092e21039299ab76715596eac01
If you have any questions, reach out to me on Linkedin!
The document provides an overview of IT service management initiatives at the Defense Information Systems Agency (DISA). It discusses DISA's mission, organization structure, and goals for adopting the Information Technology Infrastructure Library (ITIL) framework. A 5-phase approach is used to reform key IT service management processes, including defining owners and tracking progress. The goals are to improve services, optimize processes, increase standardization and meet an ISO certification.
This document provides an overview of investing in AI-driven startups. It outlines Dr. Roy Lowrance's background working with machine learning systems and startups. It then lists 100 AI startups that have raised over $11.7 billion total. The agenda covers an overview of AI, machine learning and big data, the life cycle of AI projects, and sustainable competitive advantages for AI-based startups.
Explore Microsoft's Team Data Science Process, an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently
Learn best practices for managing and creating value from data science projects
The document provides an overview of a presentation given at the 2009 IT Service Management Conference and Expo on IT service management at the Defense Information Systems Agency (DISA). The presentation covers DISA's organization, mission, and vision; its IT service management vision and goals; governance structure; and its 5-phase approach to reforming key IT service management processes based on the ITIL framework to improve service delivery and maturity levels.
CIS 2303 LO1: Introduction to System Analysis and DesignAhmad Ammari
This document provides an overview of the Systems Analysis and Design course, including describing the evolution of software development methodologies and outlining the learning objectives. It defines key terms like information systems, systems analysis and design, and IT. It also explains the systems development life cycle (SDLC) model and its phases of planning, analysis, design, implementation, and support.
How to Optimize Your Metadata and TaxonomyIXIASOFT
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2. It outlines the key steps: create a content strategy plan, determine metrics to measure goals, apply metadata to content using a metadata schema, and communicate results using reports and queries.
3. Applying metadata according to the strategy helps users find content and measures strategy success, while communicating results builds trust and credibility with stakeholders.
Improve Product Design with High Quality RequirementsElizabeth Steiner
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The document discusses best practices for collecting software project data including defining a process for collection, storage, and review of data to ensure integrity. It emphasizes personally interacting with data sources to clarify information, establishing a central repository, and normalizing data for later analysis and calibration of estimation models. The checklist provides guidance on reviewing various aspects of the data collection to validate completeness and accuracy.
This document discusses implementing a data governance program to address various data challenges. It outlines current data issues like missing information, duplicate data and integration difficulties. A data governance program is proposed to establish policies, processes, roles and data ownership to improve data quality. The presentation recommends starting with a small pilot project, then expanding organization-wide. It provides examples of creating a data dictionary to define data elements and assigning data owners.
IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
Lean pilots provide an innovative framework for solving enterprise challenges through agile and lean methodologies. Case studies highlight successes in automating contracts, streamlining third party onboarding, improving the DUNS research process, and ensuring Privacy Shield compliance. Lessons learned include welcoming early failure, using cross-functional self-organizing teams, making decisions with data, and ensuring enterprise collaboration. The framework establishes a repeatable process for running lean experiments to minimize risk and waste.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Identifying compliance gaps is a core and seemingly straightforward piece of GDPR compliance. But the way you structure the processes around gap analyses and their frequency can have a big impact on the risk you take on and the amount of extra work you create for yourself.
CFM certification, which is the Certified Facility Manager certificate that building managers can get through the International Facility Management Association (IFMA). Employers and their clients appreciate the value of CFM certification as it shows that the facility manager has the right expertise and skills to handle increasingly complicated questions regarding building management.
Unit 2 Classical Systems Development Methodology.pptxVrundaPatadia
This document discusses classical systems development methodology. It describes the classical systems development life cycle (SDLC) which includes 7 phases: preliminary investigation, determination of system requirements, design of system, development of software, system testing, implementation and evaluation, and system maintenance. It also discusses tools and techniques used in each phase like fact finding techniques, documentation tools, and decision models.
Doing Analytics Right - Designing and Automating AnalyticsTasktop
There is no “one-sized fits all” of development analytics. It is not as simple as “here are the measures you need, go implement them.” The world of software delivery is too complex, and software organizations differ too significantly, to make it that simple. As discussed in the first webinar, the analytics you need depend on your unique business goals and environment.
That said, the design of your analytics solution will still require:
* The dashboards,
* the required data, and
* an appropriate choice of analytical techniques and statistics to apply to the data.
This webinar will describe a straightforward method for finding your analytic solution. In particular, we will explain how to adapt the Goal, Question, Metric (GQM) method to development processes. In addition, we will explain how to avoid “the light is brighter here” analytics anti-pattern: the idea that organizations tend to design metrics programs around the data they can easily get, rather than figuring out how to get the data they really need.
The document provides an overview of changes to the requirements definition process at an internal company. Key points:
- A new, standardized requirements definition process is being implemented across the company to better align requirements with business needs and strategy.
- Training sessions on the new process will be held in July and August, and recorded versions will be made available.
- The process aims to reduce risks and costs through clear documentation and prioritization of requirements.
- Roles and responsibilities in the new process are defined, including for business analysts.
Building a Marketing Data Warehouse from Scratch - SMX Advanced 202Christopher Gutknecht
This deck covers the journey of starting with BigQuery, adding more data sources and building a process around your data warehouse. It covers the three phases greenfield, dashboards and operational analytics and the necessary data components.
The code for uploading your product feed can be found here:
https://gist.github.com/ChrisGutknecht/fde93092e21039299ab76715596eac01
If you have any questions, reach out to me on Linkedin!
The document provides an overview of IT service management initiatives at the Defense Information Systems Agency (DISA). It discusses DISA's mission, organization structure, and goals for adopting the Information Technology Infrastructure Library (ITIL) framework. A 5-phase approach is used to reform key IT service management processes, including defining owners and tracking progress. The goals are to improve services, optimize processes, increase standardization and meet an ISO certification.
This document provides an overview of investing in AI-driven startups. It outlines Dr. Roy Lowrance's background working with machine learning systems and startups. It then lists 100 AI startups that have raised over $11.7 billion total. The agenda covers an overview of AI, machine learning and big data, the life cycle of AI projects, and sustainable competitive advantages for AI-based startups.
Explore Microsoft's Team Data Science Process, an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently
Learn best practices for managing and creating value from data science projects
The document provides an overview of a presentation given at the 2009 IT Service Management Conference and Expo on IT service management at the Defense Information Systems Agency (DISA). The presentation covers DISA's organization, mission, and vision; its IT service management vision and goals; governance structure; and its 5-phase approach to reforming key IT service management processes based on the ITIL framework to improve service delivery and maturity levels.
CIS 2303 LO1: Introduction to System Analysis and DesignAhmad Ammari
This document provides an overview of the Systems Analysis and Design course, including describing the evolution of software development methodologies and outlining the learning objectives. It defines key terms like information systems, systems analysis and design, and IT. It also explains the systems development life cycle (SDLC) model and its phases of planning, analysis, design, implementation, and support.
How to Optimize Your Metadata and TaxonomyIXIASOFT
1. The document discusses how to optimize metadata and taxonomy by creating a content strategy plan, determining key metrics, applying metadata to content, and communicating results to stakeholders.
2. It outlines the key steps: create a content strategy plan, determine metrics to measure goals, apply metadata to content using a metadata schema, and communicate results using reports and queries.
3. Applying metadata according to the strategy helps users find content and measures strategy success, while communicating results builds trust and credibility with stakeholders.
Improve Product Design with High Quality RequirementsElizabeth Steiner
The webinar discussed improving product design through high quality requirements. It emphasized the importance of understanding stakeholders, determining real needs through concept of operations documents, writing specific but not overly specific requirements, including traceability, and using tools to automatically check requirements quality. The presenter demonstrated Innoslate's requirements management tools.
The document discusses best practices for collecting software project data including defining a process for collection, storage, and review of data to ensure integrity. It emphasizes personally interacting with data sources to clarify information, establishing a central repository, and normalizing data for later analysis and calibration of estimation models. The checklist provides guidance on reviewing various aspects of the data collection to validate completeness and accuracy.
This document discusses implementing a data governance program to address various data challenges. It outlines current data issues like missing information, duplicate data and integration difficulties. A data governance program is proposed to establish policies, processes, roles and data ownership to improve data quality. The presentation recommends starting with a small pilot project, then expanding organization-wide. It provides examples of creating a data dictionary to define data elements and assigning data owners.
IRM Data Governance Conference February 2009, London. Presentation given on the Data Governance challenges being faced by BP and the approaches to address them.
Lean pilots provide an innovative framework for solving enterprise challenges through agile and lean methodologies. Case studies highlight successes in automating contracts, streamlining third party onboarding, improving the DUNS research process, and ensuring Privacy Shield compliance. Lessons learned include welcoming early failure, using cross-functional self-organizing teams, making decisions with data, and ensuring enterprise collaboration. The framework establishes a repeatable process for running lean experiments to minimize risk and waste.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Identifying compliance gaps is a core and seemingly straightforward piece of GDPR compliance. But the way you structure the processes around gap analyses and their frequency can have a big impact on the risk you take on and the amount of extra work you create for yourself.
CFM certification, which is the Certified Facility Manager certificate that building managers can get through the International Facility Management Association (IFMA). Employers and their clients appreciate the value of CFM certification as it shows that the facility manager has the right expertise and skills to handle increasingly complicated questions regarding building management.
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2. CompTIA Data+ provides your team members with the confidence to make data analysis come to reality.
As the need for data analytics increases and more roles will be required to provide the right context and improve
communication of vital business intelligence. The process of collecting, analyzing the data and reporting it on can help drive
your company's priorities and guide in business making
CompTIA data+ is an early-career, data analytics certification that provides you with assurance that you can bring your data
analysis into life and take decision-making based on data.
Overview
The Official CompTIA Data+ training course (DA0-001) was developed by CompTIA to help those who want to be a CompTIA
Data+ candidate.
3. Verified by a rigorous evaluation to confirm the coverage in The CompTIA Data+ (DA0-001) exam objectives. The Official
CompTIA Data+ Instructor and Student Guides provide students with the necessary skills and knowledge to translate business
needs into the direction of data-driven decisions through collecting data, manipulating data, applying techniques of statistics,
and then analyzing complex data sets, in a manner that adheres to quality and governance standards throughout the whole
lifecycle of data.
Additionally, it can aid candidates in preparing for exam CompTIA Data+ certification exam.
Skills Covered in CompTIA Data+
●Improve your knowledge of the basic principles of dimensions and schemas for data as you learn about the differences
between standard data structures and file formats.
●Learn to communicate the concept of data acquisition, motives behind cleansing and profiling databases, carrying out data
manipulation, and gaining knowledge of methods for manipulating data
●Learn to apply the correct descriptive statistical techniques and summarize different types of analysis as well as critical analysis
methods.
●Learn to translate business requirements into the most appropriate visualization that can be reports or dashboards using the
correct design elements
●Improve your ability to simplify important concepts of data governance and implement techniques for ensuring data quality
Who Should Attend CompTIA Data+
●Data Analyst
●Reporting Analyst
●Marketing Analyst
●Clinical Analyst
●Operations Analyst
4. ● Business Analyst
● Business Intelligence Analyst
Data+ is a perfect certification not just for data-related professions, but all career options can benefit from the process of
analytics and knowledge of data analytics. Careers such as financial analysts, marketing specialists, human resource
analysts or health care professionals in the clinical field can improve their performance and make informed choices when
they make use of and analyze data properly.
Course Modules for CompTIA Data+
5. Module 1: Identifying Basic Concepts of Data Schemas
● Topic 1A: Identify Relational and NonRelational Databases
● Exam Objective: 1.1 Identify basic concepts of schemas and dimensions for data.
● Review Activity: Relational and Non-Relational Databases
● Topic 1B: Understand the Way We Use Tables, Primary Keys, and Normalization
● Exam Goals 1.1 Recognize the fundamental concepts of data schemas and dimensions.
● 2.3 Given a scenario, execute data manipulation techniques.
● 5.1 Summarize important data governance concept
● Video: Identifying Relationships in Data Review: Tables, Primary Keys, and Normalization
Module 2: Understanding Different Data Systems
● Topic 2A: Describe Types of Data Processing and Storage Systems
● Exam Objective: 1.1 Identify basic concepts of data schemas and dimensions.
● Review Activity: Types of Data Processing and Storage Systems
● Topic 2B: Explain How Data Changes
● Exam Objective: 1.1 Identify basic concepts of data schemas and dimensions.
● Review : Explain How Data Changes
Module 3: Understanding Types and Characteristics of Data
● Topic 3A: Understand Types of Data
● Exam Objective: 1.2 Compare and contrast different types of data
● Review Activity: Types of Data
● Topic 3B: Break Down the Field Data Types
● Exam Objective: 1.2 Compare and contrast different kinds of data
● Video: Understanding Field Data Types Review: Field Data Types
Module 4: Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
6. ● 4. How do you distinguish between structured Data from unstructured Data
● Exam Objective: 1.3 Compare and contrast the most common formats and files for data.
● Video Structured Data Vs. Structured Data vs. Review of Unstructured Data: Structured and. Unstructured Data
● Topic 4B: Recognize Different File Formats
● Exam Objective: 1.3 Compare and contrast typical formats and files used for data.
● Review Activity: File Formats
● Topic 4C: Understand the Different Code Languages Used for Data
● Exam Objective: 1.3 Compare and contrast typical formats and files used for data.
● Review-Activity: Code Languages Used for Data
Module 5: Explaining Data Integration and Collection Methods
● Topic 5A: Understand the Processes of Extracting, Transforming, and Loading Data
● Exam Objective: 2.1 Explain data acquisition concepts
● Review: The Processes of Extracting, Transforming, and Loading Data
● Topic 5B: Explain API/Web Scraping and Other Collection Methods
● Exam Objectives: 2.1 Explain data acquisition concepts.
● 1.3 Compare and contrast common formats for files and data structures.
● Review Activity: API/Web Scraping and Other Collection Methods
● Topic 5C: Collect and Use Public and Publicly Available Data
● Exam Objective: 2.1 Explain data acquisition concepts.
● Video of the process of creating an Information Set using the Census Data Review Activity: Data that is accessible and
open
● Topic 5D: Use and Collect Survey Data
● Exam Objective: 2.1 Explain data acquisition concepts.
● Video: Building a Survey and Collecting Data Review Activity: Survey Data
Module 6: Identifying Common Reasons for Cleansing and Profiling Data
● Topic 6A: Learn to Profile Data
7. ● Exam Objective: 2.2 Identify common reasons for cleaning and making profiles of data.
● Review-Activity: Learn to Profile Data
● Topic 6B: Address Redundant, Duplicated, and Unnecessary Data
● Exam Objective: 2.2 Identify common reasons for profiling and cleaning data
● Review Activity: Redundant, Duplicated, and Unnecessary Data
● Topic 6C: Work that doesn't fit the values.
● Exam Objective: 2.2 Identify common reasons for cleaning and recording data.
● Review: Missing Values
● Topic 6D: Address Invalid Data
● Exam Objective: 2.2 Identify common reasons for cleaning and recording data.
● Video: Correcting or Removing Invalid Data Review Activity: Invalid Data
● Topic 6E: Convert Data to Meet Specifications
● Exam Objective: 2.2 Identify common reasons for profiling and cleaning data
● Review-Activity: Convert Data to Meet Specifications
Module 7: Executing Different Data Manipulation Techniques
● Topic 7A: Manipulate Field Data and Create Variables
● Exam Objective: 2.3 Given a scenario, execute data manipulation techniques.
● Review Activity: Manipulate Field Data and Create Variables
● Topic 7B: Transpose and Append Data
● Exam Objective: 2.3 Given a scenario, execute data manipulation techniques.
● Video: Transposing Data and Appending Data Sets Review-Activity: Transpose and Append Data
● Topic 7C: Query Data
● Exam Objective: 2.3 Given a scenario, execute data manipulation techniques
● Video: Discovering how Joins Impact Data Results Review Activity: Query Data
Module 8: Explaining Common Techniques for Data Manipulation and Optimization
● Topic 8A: Use Functions to Manipulate Data
8. ● Exam Objective: 2.3 Given a scenario, execute data manipulation techniques.
● 2.4 Define the most common methods for manipulating data as well as optimizing queries.
● Video: Using Functions to Manipulate Data Review-Activity: Functions to Manipulate Data
● Topic 8B: Use Common Techniques for Query Optimization
● Exam Objective: 2.4 Explain common techniques to manipulate data as well as the optimization of queries
● Review Activity: Common Techniques for Query Optimization
Module 9: Applying Descriptive Statistical Methods
● Topic 9A: Use Measures of Central Tendency
● Exam Objectives 3.1 If you're given the opportunity to describe a situation, you must use appropriate statistics.
● Video: Calculating the Measure of Central
● Tendency Review-Activity: Measures of Central Tendency
● Topic 9B: Use Measures of Dispersion
● Examination Objectives 3.1 If you are in a crisis, utilize the appropriate descriptive statistical methods
● Review Activity: Measures of Dispersion
● Topic 9C: Use Frequency and Percentages
● The Objective of the Exam 3.1 If you're given an event, use appropriate descriptive statistical methods.
● Video: Calculating Percentages Review-Activity: Frequency and Percentages
Module 10: Describing Key Analysis Techniques
● Topic 10A: Begin with an Analysis
● Exam Objective: 3.3 Summarize types of analysis as well as the most significant methods of analysis.
● Review Activity : Review Activity Start by Getting Started with Analysis
● Topic 10B: Recognize Types of Analysis
● Exam Objective: 3.3 Summarize types of analysis and the most crucial methods of analysis.
● Review-Activity: Types of Analysis
Module 11: Understanding the Use of Different Statistical Methods
9. ● Topic 11A: Understand the Importance of Statistical Tests
● The objective of the exam includes: 3.2 Explain the purpose of statistical inference techniques.
● Video: Understanding the Importance of Statistical Tests.
● Review Activity: The Importance of Statistical Tests
● Topic 11B: Break Down the Hypothesis Test
● The exam's objective includes: 3.2 Explain the purpose of statistical inference techniques.
● Review-Activity: The Hypothesis Test
● Topic 11C: Understand Tests and Methods to Determine Relationships Between Variables
● The exam's objective includes: 3.2 Explain the purpose of statistical inference.
● Review Activity: Tests and Methods to Determine Relationships Between Variables.
Module 12: Using the Appropriate Type of Visualization
● Topic 12A: Use Basic Visuals
● Exam Scope: 4.4 In the event that you're faced with a scenario, you must use the appropriate method of visualization.
● Review-Activity: Basic Visuals
● Topic 12B: Build Advanced Visuals
● Examination Objectives 4.4 In a particular scenario, apply the appropriate type of visualization.
● Video: Building and Reading Stacked Charts Review Activity: Advanced Visuals
● Topic 12C: Create Maps with Geographical Data
● The Objective of the Exam 4.4 In a particular scenario, apply the proper type of visualization.
● Review-Activity: Maps with Geographical Data
● Topic 12D: Use Visuals to Tell a Story
● Exam Scope: 4.4 When you're confronted with a situation, make use of the appropriate type of visualization.
● Review Activity: Visuals to Tell a Story
Module 13: : Expressing Business Requirements in a Report Format
● Topic 13A: Consider Audience Needs When Developing a Report
10. ● Exam Objectives: 4.1 On the basis of a specific scenario which requires business analysis Translate the requirements into
an outline.
● Review Activity: Audience Needs When Developing a Report
● Topic 13B: Describe Data Source Considerations For Reporting
● Exam Objective: 4.3 Given a scenario, use appropriate methods for dashboard development.
● Video: Accessing Source Data and Creating Reports Review Activity: Data Source Considerations for Reporting
● Topic 13C: Describe Considerations for Delivering Reports and Dashboards
● The Objective of the Exam 4.1 In a scenario that requires analysis of business Translate the needs into reports.
● 4.3 Given a scenario, use appropriate methods for dashboard development.
● Review Activity: Considerations for Delivering Reports and Dashboards.
● Topic 13D: Develop Reports or Dashboards
● The exam's goal 4.1 In light of a particular scenario and a business need to translate it into an article.
● 4.3 Given a scenario, use appropriate methods for dashboard development.
● Video: Selecting Different Visualization Layouts Review Activity: Develop Reports or Dashboards
● Topic 13E: Understand Ways to Sort and Filter Data
● Exam Objectives 4.1 In the event of a scenario and an enterprise requirement take it and translate it into an outline.
● 4.3 Given a scenario, use appropriate methods for dashboard development
● Review Activity: Ways to Sort and Filter Data
Module 14: Designing Components for Reports and Dashboards
● Topic 14A: Design Elements for Reports and Dashboards
● The exam's objective 4.2 In a specific scenario you're given create the appropriate elements for dashboards and reports.
● Video: Using Appropriate Design Components for Reports and Dashboards. Review Activity: Design Elements for
Reports/Dashboards
● Topic 14B: Utilize Standard Elements
● Exam Scope: 4.2 When you're in a position to do so, utilize suitable design elements to create dashboards and reports.
● Review Activity: Standard Elements for Reports and Dashboards
● Topic 14C: Creating a Narrative and Other Written Elements
11. ● Examination Objectives 4.2 If you're faced with an opportunity that requires the design, you must utilize the proper
components for dashboards and reports.
● Review Activity: Narrative and Other Written Elements
● Topic 14D: Understand Deployment Considerations
● Exam Objective: 4.3 Given a scenario, use appropriate methods for dashboard development
● Video: Deployment Considerations. Review Activity: Deployment Considerations
Module 15: Distinguishing Different Report Types
● Topic 15A: Understand How Updates and Timing Affect Reporting
● Exam Objectives: 4.5 Compare and contrast various types of reports.
● Review Activity: How Updates and Timing Affect Reporting
● Topic 15B: Differentiate Between Types of Reports
● Exam The scope of the exam: 4.5 Compare and contrast different kinds of reports.
● Review Activity: Types of Reports
Module 16: Summarizing the Importance of Data Governance
● Topic 16A: Define Data Governance
● Exam Objective: 5.1 Summarize important data governance concepts.
● Video: Importance of Data Governance
● Review Activity: Data Governance
● Topic 16B: Understand Access Requirements and Policies
● Exam Objective: 5.1 Summarize important data governance concepts.
● Review Activity: Access Requirements and Policies
● Topic 16C: Understand Security Requirements
● Exam Objective: 5.1 Summarize important data governance concepts.
● Review Activity: Security Requirements
● Topic 16D: Understand Entity Relationship Requirements
● Exam Objective: 5.1 Summarize important data governance concepts.
12. ● Review Activity: Entity Relationship Requirements
Module 17: Applying Quality Control to Data
● Topic 17A: Describe Characteristics, Rules, and Metrics of Data Quality
● Exam Objective: 5.2 Given a scenario, apply data quality control concepts.
● Review Activity: Characteristics, Rules, and Metrics of Data Quality
● Topic 17B: Identify Reasons to Quality Check Data and Methods of Data Validation
● Exam Objective: 5.2 Given a scenario, apply data quality control concepts.
● Review Activity: Reasons to Quality Check Data and Methods of Data Validation
Module 18: Explaining Master Data Management Concepts
● Topic 18A: Explain the Basics of Master Data Management
● Exam Objective: 5.3
● Video: Understanding Data Management. Review Activity: The Basics of Master Data Management
● Topic 18B: Describe Master Data Management Processes
● Exam Objective: 5.3 Explain master data management (MDM) concepts.
● Review Activity: Master Data Management Processes
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